{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "##解决图例中文乱码，设置参数\n",
    "matplotlib.rcParams['font.family']=['sans-serif']\n",
    "matplotlib.rcParams['font.sans-serif']=['Songti SC']#显示中文\n",
    "matplotlib.rcParams['font.serif']=['Songti SC']\n",
    "matplotlib.rcParams['axes.unicode_minus']=False #正常显示符号\n",
    "\n",
    "#屏蔽警告信息\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 基础箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv('seaborn-data/iris.csv')\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "plt.title(\"基础箱线图\")\n",
    "sns.boxplot( x=df[\"species\"], y=df[\"sepal_length\"] )\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"显示底层数据分布\")\n",
    "sns.boxplot( x=\"species\", y=\"sepal_length\", data=df )\n",
    "sns.stripplot(x=\"species\", y=\"sepal_length\", data=df, color=\"orange\", jitter=0.2, size=2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分组箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=pd.read_csv('seaborn-data/tips.csv')\n",
    "sns.boxplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=df2, palette=\"Set1\", width=0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
